There are two alternative accounts for the data from the first two experiments. First, it is possible that the observed effect of training with the tool is a product of elevated arousal, rather than sensorimotor training. By this account, playing the game is more exciting than watching videos of the same scenes and the elevated arousal might increase attention to threats, reducing attention to peripheral stimuli and reducing change blindness. This logic is akin to the original weapon focus literature (Fawcett, Russell, Peace, & Christie, 2013). To test this alternative, we required a training condition that does not involve the same postures or actions as holding the simulated weapon, but is equally arousing. To this end, we collected data in a condition where participants trained on the game using a controller that required an awkward posture and was not weapon-like.
Second, the object that is held during training is confounded with the training itself; participants held a ball and watched or held a simulated weapon and played. To resolve this confound, we collected data in a condition where participants held the weapon-like controller and watched the videos passively.
We compared both new conditions against a replicated condition of the weapon–weapon condition of Experiment 2, with the expectation that this active weapon condition should produce faster change detection times than the other conditions.
Method
Participants
Thirty-three students participated for course credit or financial compensation (26 women; mean age = 21.21 years; SD = 3.16).
Materials and procedure
The method was identical to Experiments 1 and 2 with the following exceptions. The experiment was conducted at a different institution, on a large flat-screen monitor instead of a projector. In the active weapon condition, participants played the game with the weapon, then conducted the search holding the weapon (replication of weapon–weapon condition from Experiments 1 and 2). In the passive weapon condition, participants held the weapon and watched the screen-grabbed videos, then searched while holding the weapon. In the Wiimote condition, participants played the game during training but used a different, non-weapon controller. The standard Wiimote controller is an elongated, rectangular prism that players point at the screen. Connected to it by a long wire is a smaller “nunchaku” controller with a joystick. Players were instructed to play the game with the wand and to hold the nunchaku behind their back. They searched the display with the same wand controller in hand.
Results and discussion
Responses for which the participant failed to identify the change before the trial timed out (40 s) or incorrectly identified the change were categorized as errors. Unlike Experiments 1 and 2, the training and search factors were not fully factorial, so treating the three conditions as a between-subjects factor is appropriate. Moreover, the lack of training and search interactions with change type motivated us to focus on the comparison between the weapon–weapon condition and the two control conditions.Footnote 2 Consequently, mean RTs for correctly identified changes were entered into a between-subjects ANOVA contrast between the weapon–weapon condition and the other two conditions, where we observed a significant effect, F(1,31) = 5.79, p = 0.022, confirming that participants in the weapon–weapon condition detected changes faster than the controls (Fig. 5). The same contrast was conducted on the mean number of errors; there was no effect (F < 1).
Participants who played the game detected changes faster than participants who held onto the simulated weapon and watched the game or participants who played the game with a controller that used a non-weapon posture. These results resolve the confounding of training object and training task in Experiment 2 and, in addition to replicating our result, they rule out the alternative explanation that the present training effects are caused by general arousal or engagement with the game.
General discussion
Across three experiments, participants trained and searched for changes while holding a weapon or a ball. During training, participants using the simulated weapon could actively destroy on-screen elements, whereas participants using the ball could not. We found that participants who had trained with the weapon were later better able to detect changes to stimuli regardless of the object that changed (agent, object, or feature of the environment). This result shows that vigilant surveillance benefits from sensorimotor training with the tool – in this case, a simulated weapon – that you would use to interact with the environment.
Another important finding is the null effect of the search tool. It made no difference whether observers searched with the weapon or ball. This violated our expectations, given that action affordances flexibly alter attentional allocation during change detection (Symes et al., 2008) and that tool use in general – and guns specifically – alter attentional location (Biggs et al., 2013). Accordingly, we predicted that holding the weapon during search would confer advantages, or at least produce biases, compared with holding the ball.
We can, however, cautiously interpret a marginal interaction between the training tool and the search tool in Experiment 2. Participants who trained and searched with the ball were slowest to detect changes across the board, whereas participants who trained with the ball (no sensorimotor training; only visual) and searched with the gun expressed search latencies more like the groups who trained with the gun. It appears that any exposure to the weapon, whether during training or search, was sufficient to attenuate change blindness. The tantalizing implication drawn from this result is that participants who trained with the ball but searched with the weapon were as capable of simulating the weapon-related action affordances during search as those who trained with the weapon.
Interestingly, experience with the weapon led to an item-non-specific attenuation of change blindness. In other words, changes of all types were detected faster after training with the weapon. If the observed attenuation of change blindness is indeed caused by variations in attentional allocation resulting from simulated weapon affordances, then we might expect that shootable elements – agents and objects – should be prioritized during search over environmental elements in the weapon conditions. Instead, we found that simulated weapon training led to a blanket improvement in change detection times regardless of change type, suggesting that change detection is universally improved by sensorimotor training. This finding sets the current study apart from existing research on domain-specific expertise and change blindness. Experts in solving physics problems (Feil & Mestre, 2010) or watching football (Werner & Thies, 2000) are faster at detecting changes to domain-relevant images as compared to novices. However, these studies examine true expertise derived from perceptual experience that goes well beyond the ~10 min practiced by our participants. Moreover, the item-non-specific attenuation of change blindness we observed sets the present findings apart from domain-relevant attenuation of change blindness. A domain-relevant effect in the present study would occur only for shootable things (agents and objects). We found no interaction between training tool and change category, indicating that the attenuation of change blindness was not domain-relevant as it is in existing investigations of perceptual expertise.
Additionally, the effect of change type, wherein changes to agents were detected faster than inanimate elements, draws comparisons to the animate monitoring hypothesis (New et al., 2007). It is not surprising that agents should be detected fastest, especially considering that they were all threatening stimuli in the present study, however it should be noted that destructible objects were detected much faster than environmental elements. In previous studies examining animacy and change detection, artifacts and topographical landmarks are detected equally quickly (New et al., 2007). In contrast, we found a large advantage for objects over environments. We propose that objects’ affordance for destruction led to improved attentional allocation over the environmental changes.
In many real-world scenarios, the critical change – for example, the sudden appearance of a threat – occurs only once, rather than alternating as in the flicker paradigm. For this reason, it would be helpful to conduct future investigations using the one-shot change detection paradigm, where an image is presented for a short duration, followed by an intervening mask and a slightly changed image (e.g. Phillips, 1974). We used the flicker paradigm, where the images alternate repeatedly, because we reasoned the potential for weapon use would occur in scenarios where the observer was engaged in prolonged surveillance rather than a brief exposure. Both methods provide unique insights into the nature of change detection (Rensink, 2002) and the applied question of change detection during vigilant surveillance. For example, the flicker paradigm is better suited for measuring the speed of change detection, whereas the one-shot paradigm typically measures accuracy.
Experiment 3 was conducted with the intent of isolating the best control conditions to compare against the weapon–weapon training-search condition. Because of these comparisons, we can confidently attribute our finding to sensorimotor training with a simulated weapon: change blindness was attenuated following simulated training with a weapon compared to visual-only and arousal-matched control conditions. Another interesting comparison would have been a no-training condition, where participants had no exposure to the stimuli at all prior to the change detection task. The marginal interaction between training and search conditions in Experiment 2 suggests it is possible that sensorimotor training is not required to attain this improved change detection; searching with the simulated weapon may be sufficient. Consequently, it may be possible that an observer with no prior exposure at all to the stimuli could achieve a similar improvement in change detection just by holding a weapon. This speculative result imagines a strong weapon affordance effect on attention.
In the real world, training regimens for police and military duties extend far beyond the ~10 min practiced by our participants. Because the training period was short, and because there was no delay between training and search, we can only speak of short-term effects on vision. The effect of simulated weapon training can be attributed to sensorimotor experience, but it is possible that long-term training effects can be elicited with only sensory training. More research is necessary to determine whether sensory-only training can produce long-term improvements in attentional allocation in sentry-type tasks. The restriction of our conclusions to the short term reveals an additional point of interest: that we observed attenuated change blindness as a result of simulated weapon training indicates that these types of training affordances incur immediate effects on attentional allocation, consistent with a rapid integration of tool affordances into the body schema. This is concordant with some of our earlier research showing that brief exposure to a new tool stimulus is sufficient to alter the allocation of attention to and near that tool (Taylor & Witt, 2014, Experiment 4; also Reed et al., 2010).
In conclusion, training with a simulated weapon led to immediate item-non-specific attenuation of change blindness. This effect reveals the importance of training regimens when it comes to the use of weapons and more generally suggests that sensorimotor experience with a tool is essential to activate action-related attentional priorities.